Gene Ontology

Gene Ontology est un projet bio-informatique destiné à structurer la description des gènes et des produits géniques dans le cadre d'une ontologie commune à toutes les espèces. Ce projet, qui s'inscrit dans la démarche plus large d'Open Biomedical Ontologies (OBO) regroupant d'autres projets bio-informatiques dans le domaine biomédical, poursuit trois objectifs :

gérer et enrichir son vocabulaire contrôlé décrivant les gènes et leurs produits,
gérer les annotations, c'est-à-dire les informations rattachées aux gènes et à leurs produits,
fournir les outils permettant d'accéder aux informations structurées dans le cadre du projet.

La base GO est conçue comme un graphe orienté acyclique, chaque terme étant en relation avec un ou plusieurs termes du même domaine, et parfois d'autres domaines. Le vocabulaire GO est construit pour n'être pas dépendant des espèces considérées, avec des termes applicables à la fois aux organismes multicellulaires et unicellulaires, aux eucaryotes et aux

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Bio-informatique

La bioinformatique (ou bio-informatique), est un champ de recherche multidisciplinaire de la biotechnologie où travaillent de concert biologistes, médecins, informaticiens, mathématiciens, physicien

Base de données biologiques

Les bases de données biologiques sont des bibliothèques répertoriant des informations sur les sciences de la vie collectées grâce à des expériences scientifiques, à la littérature publiée, aux techn

KEGG

KEGG (Kyoto Encyclopedia of Genes and Genomes) désigne un ensemble de bases de données relatives aux génomes, aux voies métaboliques et aux composés biochimiques. Il a été lancé avec le programme japo

CH-419: Protein mass spectrometry and proteomics

In systems biology, proteomics represents an essential pillar. The understanding of protein function and regulation provides key information to decipher the complexity of living systems. Proteomic technology now enables deep quantitative proteome mapping via mass spectrometry.

BIOENG-420: Single cell biology

The students are exposed to experimental and analytical approaches specific to single cell biology, with an emphasis on quantitative aspects.

Homoeolog Inference Methods Requiring Bidirectional Best Hits or Synteny Miss Many Pairs

Shaoline Sheppard

Homoeologs are pairs of genes or chromosomes in the same species that originated by speciation and were brought back together in the same genome by allopolyploidization. Bioinformatic methods for accurate homoeology inference are crucial for studying the evolutionary consequences of polyploidization, and homoeology is typically inferred on the basis of bidirectional best hit (BBH) and/or positional conservation (synteny). However, these methods neglect the fact that genes can duplicate and move, both prior to and after the allopolyploidization event. These duplications and movements can result in many-to-many and/or nonsyntenic homoeologs-which thus remain undetected and unstudied. Here, using the allotetraploid upland cotton (Gossypium hirsutum) as a case study, we show that conventional approaches indeed miss a substantial proportion of homoeologs. Additionally, we found that many of the missed pairs of homoeologs are broadly and highly expressed. A gene ontology analysis revealed a high proportion of the nonsyntenic and non-BBH homoeologs to be involved in protein translation and are likely to contribute to the functional repertoire of cotton. Thus, from an evolutionary and functional genomics standpoint, choosing a homoeolog inference method which does not solely rely on 1:1 relationship cardinality or synteny is crucial for not missing these potentially important homoeolog pairs.

OXFORD UNIV PRESS2021

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Enhancing Session-Based Recommendations through Sequential Modeling

Boi Faltings, Stéphane Bernard Martin, Vincent Schickel

Recommender systems typically determine the items they should recommend by learning models of user-preferences. Most often, those preferences are modeled as static and independent of context. In real life however, users consider items in sequence: TV series are watched episode by episode and accessories are chosen after the main appliance. Unfortunately, since sequences are more complex to model, they are often not taken into account. We developed an efficient sequence-modeling approach based on Bayesian Variable-order Markov Models and combined it with an existing content-based system, the Ontology Filtering. We tested this approach through live evaluations on two e-commerce sites. It dramatically increased performance, more than doubling the CTR and strongly increasing recommendation-mediated sales. These tests also confirm that the technique works efficiently and reliably in a production setting.

ASSOC COMPUTING MACHINERY2018

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Context-Tree Recommendation vs Matrix-Factorization: Algorithm Selection and Live Users Evaluation

Boi Faltings, Stéphane Bernard Martin, Vincent Schickel

We describe the selection, implementation and online evaluation of two e-commerce recommender systems developed with our partner company, Prediggo. The first one is based on the novel method of Bayesian Variable-order Markov Modeling (BVMM). The second, SSAGD, is a novel variant of the Matrix-Factorization technique (MF), which is considered state-of-the-art in the recommender literature. We discuss the offline tests we carried out to select the best MF variant, and present the results of two A/B tests performed on live ecommerce websites after the deployment of the new algorithms. Comparing the new recommenders and Prediggo’s proprietary algorithm of Ontology Filtering, we show that the BVMM significantly outperforms the two others in terms of CTR and prediction speed, and leads to a strong increase in recommendation-mediated sales. Although MF exhibits reasonably good accuracy, the BVMM is still significantly more accurate and avoids the high memory requirements of MF. This scalability is essential for its application in online businesses.

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2019

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